AZ63/Ti/Zr Nanocomposite for Bone-Related Biomedical Applications

Considering the unique properties of magnesium and its alloy


Introduction
Currently replacing a fractured bone with an artificial implant in alloy material is one of the innovative research studies [1]. Most of the studies considered the magnesium material in the implant process due to its excellent mechanical properties and lifetime [2]. Currently, in orthopaedic applications, the materials are needed to be highly corrosion-resistant but degradable. Now, the research work focused on iron-based biodegradable materials used in the orthopaedic function [3][4][5]. Researchers are taking vast studies to improve the implant material properties through the reinforcement of alloying materials, surface coating, and innovative process methods [6]. There is no divergence of human normal physiological functions even in injured bone by replacing artificial bone implant techniques. The bone implant is reflecting the need for treatment for the normal functioning of joints and also ensures the treatment superiority [7][8][9]. Now, numerous biomaterials are developed for bone renovation applications such as bioceramics, biometals, and biopolymers [10]. Of all these materials, magnesium and its alloys are the innovative materials for bone implants. Compared with that of the human bone, Young's modulus of the magnesium alloys has similar value; hence, it is an apt material for bone replacement [11][12][13]. Similarly, the density of the magnesium alloy is 1.79 g/cm 3 , while it was denoted that the closer density value of the human bone is 1.75 g/cm 3 . Titanium and its alloys played a vital role in biomedical applications due to their higher Young's modulus and strength in contrast to those of the human bone [14][15][16]. Magnesium alloy is one of the encouraging materials for the control of processing and making good biomedical parts. Magnesium alloys possess excellent degradation uniqueness. Of all minerals present in the human body, magnesium has taken the fourth place [17]. It is an indispensable element for building bone and soft tissue. Generally, the magnesium alloy bone replacement is classified into two categories such as bone fixation device and bone    BioMed Research International tissue engineering scaffold [5,18,19]. Normally, the bone screw, bone pin, and bone plates are named bone fixation devices; these are essential parts in repairing an affected bone. Recently, the Mg and Ti alloys have a higher level of usage in the bone fixation device due to their higher modulus compared to natural bone [20]. Zirconium is also the major element used in bone repair; now, hybrid composites are to be developed in the bone implant material [21]. The research gap is AZ63 magnesium alloy property modification through titanium and zirconium nanoparticles at different reinforcement contributions in the nanocomposite matrix. The investigation is aimed at modifying the wear as well as corrosive resistance positively in the application requirement ranges. "Can wear resistance and corrosive resistance of AZ63 Magnesium alloy modify positively by reinforcing titanium and zirconium nanoparticles?" is an unanswered question, and this manuscript tried answering the same question. This experimental work is considering the AZ63 magnesium alloy reinforced with nanoparticles of titanium and zirconium for the fabrication of magnesium alloy hybrid composites. These composites are prepared by using the stir casting process. Taguchi analysis was used to analyse the wear and corrosion performance of the composites through optimization of the process parameters [22].

Experimental Procedure
In this composite preparation, the base material is taken as the AZ63 magnesium alloy and the reinforcement nanoparticles are titanium and zirconium. All these base and reinforcement materials are purchased from Exclusive Magnesium Private Limited, Hyderabad. The average particle size of the titanium and zirconium was 30-70 nm [23]. Composite preparation is carried out through a bottom pouring-type stir casting apparatus [24]. Figure 1 illustrates the experimental work as a flow diagram.
In the stir casting process, the magnesium alloy is heated up to 650°C in the furnace for 3 hours [25]. Similarly, the reinforced nanoparticles of titanium and zirconium equally contributed at different wt% of reinforcement (3%, 6%, 9%, and 12%). They were preheated in the preheat chamber with a temperature level of 900°C. The preheating process is maintained for 2 hours for excellent blending of reinforced nanoparticles [26][27][28]. After that, the preheated reinforced molten material is added to the magnesium melt, and the homogeneous mixture is achieved through stirring action by using stirring mechanism with an electrical controller [29]. The mixture of base material melts and reinforced nanoparticle melt in the furnace is heated at 500°C. Finally, the molten material is poured into the prepared die and allowed to cool [30]. The raw sample was separated from the die and sliced for the required dimensions for preparing samples to investigate the wear and corrosive properties.
A dry sliding wear test is conducted through Pin-on-disc apparatus (DUCOM 20 LE model) as shown in Figure 2(a). The wear test is conducted as per the ASTM G-99 standard; the dimensions of the specimens are 13 mm in diameter and 35 mm in length as shown in Figure 2(b). Different process parameters and levels to be considered to conduct the wear test are the percentage of reinforcement, load disc speed, and sliding distance [31,32].
A salt spray test is conducted to estimate the corrosion rate of the magnesium alloy hybrid composites; Figure 3 illustrates the salt spray corrosion test specimens. As per the ASTM standard B117, the different parameters considered for conducting corrosion tests are reinforcement (%), NaCl (%), pH value, and exposure time [33]. Before conducting of corrosion test, the specimen surfaces are abraded by using 600 grit size emery sheets.
The factors and their levels were furnished for wear and corrosion analysis and are presented in Tables 1 and 2, respectively.

Results and Discussion
3.1. Wear Test. The minimum wear rate was recorded as 0.0078mm 3 /m by the influence of 12% reinforcement, 60 N of load, 1 m/s of disc speed, and 1500 m of sliding distance (m). Similarly, higher wear was registered as 0.0255 mm3/ m as presented in Table 3.    Signal-to-noise: Smaller is better  Table 4 presents the higher influencing parameter of the wear test based on the rank and delta value. In the wear test analysis, a higher influence factor was found as disc speed followed by sliding distance, load, and percentage of reinforcement. The optimal parameters of the wear test are 12% of reinforcement, 15 N of load, 1 m/s of disc speed, and 1700 m of sliding distance.
The increase of reinforcement percentage reduces the wear rate of the 12% of reinforced offered minimum wear rate of the hybrid magnesium alloy composites as shown in Figure 4. A minimum applied load such as 15 N provided a minimum wear rate; continually increasing load, the wear rate can be increased simultaneously. Lower disc speed offered a lower wear rate; increasing the disc speed from 1 m/s to 3 m/s, the wear rate also increased. Minimum sliding distance increases the wear rate; the increase of sliding distance from 1000 m to 1700 m the wear rate was reduced.     Higher contributions of parameters in the wear rate analysis are presented in Table 5. ANOVA results concluded that a higherF value indicates a higher contribution of that parameter. In this analysis, as the P values were found to be below 0.05, the selected parameters and their influence were significant.
The prediction model for wear rate prediction is presented in equation (1). As R 2 > 95%, the model will give good agreement with the experimental results. ð1Þ Figure 6 presents the contribution of each parameter in the wear test. Disc speed highly contributed (21.1%), and the load contributed 19%. An equal contribution of 14.6% was recorded for sliding distance and reinforcement factors.
The experimental responses of wear rates were found to be closer to the predicted wear rate as shown in Figure 7. From both the wear rate analyses, experimental wear rate values were closer to predicted ones; hence, the output of the wear analysis was found to have better accuracy.
Two parameters of correlation are presented in Figure 8 through a parallel set plot. Figure 8(a) represents the 12% of reinforcement and 60 N of load offered minimum wear rate. Figure 8(b) correlates that the 60 N of applied load and 1 m/s of disc speed recorded minimum wear rate. Figure 8(c) illustrates that 1 m/s of disc speed and 1500 m of sliding distance offered a lower wear rate. Figure 8(d) presents the 1500 m of sliding distance and 12% reinforcement provided minimum wear rate.    Figure 9: Main effects plot for S/N ratios (corrosion rate).

Salt Spray Corrosion Test.
The salt spray corrosion test demonstrates the minimum and maximum corrosion rates were obtained through different parameters' influence. The minimum corrosion rate was recorded as 0.0076 mm/year by the influence of 6% reinforcement, 5% of NaCl, a pH value of 9, and 24 hrs of exposure time. On the contrary, the maximum corrosion rate was found as 0.211 mm/year; it is presented in Table 6. All the experimental values are very close to the predicted values. Table 7 presents the higher level and lower level influencing parameters of the corrosion rate analysis; it can      BioMed Research International be achieved by rank order and delta value. In corrosion rate analysis, a higher priority parameter was found as % of NaCl, followed by pH value, reinforcement (%), and exposure time. The optimal parameters for the minimum corrosion rate analysis are 6% of reinforcement, 5% of NaCl, and maintaining the pH value of 6 for 24 hrs of exposure time.
In the corrosion rate analysis, until 6% of reinforcement corrosion rate decreases (due to increasing of Signal to noise ratio), then increases (refer to Figure 9). Until NaCl concentration improved to 5%, the corrosion rate decreased and then declines. The lower pH value (6) registered a minimum corrosion rate than higher pH values. Similarly, the shorter exposure time offered a minimum corrosion rate. Figure 10 illustrates the normal probability plot for the corrosion test, for this plot exhibits the involvement of data points in the corrosion rate analysis. Few of the data points only deviated from the mean line; the remaining values fall closer to the mean line. So it can be proved that the selected parameters were good. Table 8 presents the contributions of various factors. F values correlate with the contribution per-centage of each parameter considered, in the corrosion rate inspection. In this analysis, the NaCl % was extremely contributed.
The prediction model for corrosion rate prediction is presented in equation (2). As R 2 > 95%, the model will give good agreement with experimented results.
Corrosion rate × 10 −3 mm/year ð Þ = 0:1184 + 0:00268 reinforcement % ð Þ − 0:02500 NaCl % ð Þ + 0:01225 pH value + 0:000217 exposure time hrs ð Þ: ð2Þ Figure 11 presents the higher contribution and lower contribution of the parameters in the corrosion rate analysis. Sodium chloride percentage (NaCl %) was extremely contributed (53.9%), followed by 12.9% of pH value, 5.6% of reinforcement, and lower percentage (0.6%) of exposure time. The observed corrosion rates were nearer to the predicted corrosion rates (refer to Figure 12); therefore, the output result of the corrosion rate analysis is accurate. Figure 13 illustrates the correlation between the parameters, with heatmap analysis. Figure 13(a) exemplifies the 6% reinforced sample in the 5% NaCl-concentrated solution offered a minimum corrosion rate. Figure 13(b) demonstrates that the 5% of NaCl concentration at the pH value of 9 registered a minimum corrosion rate. Figure 13(c) illustrates that maintaining the pH value of 9 for 24 hrs of exposure time recorded a lower corrosion rate. Figure 13(d) demonstrates the 24 hrs of exposure time and 6% of reinforcement registered minimum corrosion rate.

Conclusion
Magnesium alloy hybrid nanocomposites for biomedical applications were prepared through the stir casting process. The wear and corrosive properties were analysed in the prepared nanocomposite samples successfully. The results of this investigation were drawn as follows: (i) In the wear test, the minimum wear rate was recorded as 0.0078 mm 3 /m for the 12% reinforced nanocomposite sample at 60 N of load on the pin and 1 m/s of disc speed for the 1500 m of sliding distance (m). On the contrary, higher wear was found at 0.0255 mm 3 /m. Optimal parameters of the wear test attained 12% of reinforced nanocomposite sample with 15 N load on the pin at 1 m/s of disc speed for 1700 m of sliding distance (ii) Higher contribution of the parameter in the wear test was recorded as disc speed was highly contributed (21.1%), followed by 19% of load and equal contribution (14.6%) which was observed for the factor percentage of reinforcement and sliding distance (iii) In the corrosion rate analysis, a minimum corrosion rate was found to be 0.0076 mm/year for the 6% reinforced nanocomposite sample in 5% NaCl concentrated solution with a pH value of 9 for 24 hrs of exposure time. On the contrary, the maximum corrosion rate was registered as 0.211 mm/year. Optimal parameters of the corrosion rate analysis were observed as 6% of reinforced nanocomposite sample in the 5% NaCl concentrated solution with the pH value of 6 for 24 hrs of exposure time (iv) Sodium chloride percentage (NaCl %) was exceptionally contributed (53.9%), followed by 12.9% of pH value, 5.6% of reinforcement %, and lower percentage (0.6%) of exposure time From the above experimental results, it can be understood that the maximum reinforcement outperformed in promoting the wear-resistance properties of the proposed nanocomposite. Similarly, in the salt spray corrosion test, the same kind of trend was observed. Hence, the increase in reinforcement improved wear resistance and corrosive resistance. As it has reached a maximum value as optimal, there is a chance to improve such resistance by increasing the reinforcement percentage and a maximum possible reinforcement may be found in future research.

Data Availability
The data used to support the findings of this study are included in the article. Should further data or information be required, these are available from the corresponding author upon request.

Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper. This study was performed as a part of the authors' employment at Hawassa University, Ethiopia.